week 2 apply assignment

Provide details on what you need help with along with a budget and time limit. Questions are posted anonymously and can be made 100% private.

Studypool matches you to the best tutor to help you with your question. Our tutors are highly qualified and vetted.

Your matched tutor provides personalized help according to your question details. Payment is made only after you have completed your 1-on-1 session and are satisfied with your session.

• Homework Q&A
• Become a Tutor

All Subjects

Mathematics

Programming

Health & Medical

Engineering

Computer Science

Foreign Languages

Access over 20 million homework & study documents

Week 2 apply assignment.

Sign up to view the full document!

24/7 Homework Help

Stuck on a homework question? Our verified tutors can answer all questions, from basic  math  to advanced rocket science !

Similar Documents

working on a homework question?

Studypool is powered by Microtutoring TM

Copyright © 2024. Studypool Inc.

Studypool is not sponsored or endorsed by any college or university.

Ongoing Conversations

Access over 20 million homework documents through the notebank

Get on-demand Q&A homework help from verified tutors

Read 1000s of rich book guides covering popular titles

Sign up with Google

Sign up with Facebook

Already have an account? Login

Login with Google

Login with Facebook

Don't have an account? Sign Up

• Top Courses
• Online Degrees
• Find your New Career
• Join for Free

Predictive Modeling and Analytics

This course is part of Advanced Business Analytics Specialization

Taught in English

Some content may not be translated

Instructor: Dan Zhang

Financial aid available

35,788 already enrolled

(590 reviews)

What you'll learn

Apply exploratory data analysis to gain insights and prepare data for predictive modeling

Summarize and visualize datasets using appropriate tools

Identify modeling techniques for prediction of continuous and discrete outcomes

Identify appropriate graphs to explore and display datasets

Skills you'll gain

• Regression Analysis
• Data Cleansing
• Predictive Modelling
• Exploratory Data Analysis

Details to know

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

Welcome to the second course in the Data Analytics for Business specialization!

This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. You’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. The expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.

Exploratory Data Analysis and Visualizations

At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets

What's included

8 videos 1 reading 2 quizzes 1 peer review 1 discussion prompt

8 videos • Total 37 minutes

• Introduction to the Course • 1 minute • Preview module
• 0. Introduction to the Module. Why Exploratory Data Analysis is Important • 3 minutes
• 1. Data Cleanup and Transformation • 4 minutes
• 2. Dealing With Missing Values • 6 minutes
• 3. Dealing with Outliers • 3 minutes
• 4. Adding and Removing Variables • 4 minutes
• 5. Common Graphs • 7 minutes
• 6. What is Good Data Visualization? • 4 minutes

1 reading • Total 10 minutes

• Register for Analytic Solver Platform for Education (ASPE) • 10 minutes

2 quizzes • Total 60 minutes

• Week 1 Application Assignment 1 (optional): Data Cleanup • 30 minutes
• Week 1 Quiz • 30 minutes

1 peer review • Total 60 minutes

• Week 1 Application Assignment 2: Data Visualization • 60 minutes

1 discussion prompt • Total 10 minutes

• Data Exploration • 10 minutes

Predicting a Continuous Variable

This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.

8 videos 2 quizzes 1 discussion prompt

8 videos • Total 40 minutes

• 0. Introduction to Predictive Modeling • 2 minutes • Preview module
• 1. Introduction to Linear Regression • 8 minutes
• 2. Assessing Predictive Accuracy Using Cross-Validation • 5 minutes
• 3. Multiple Regression • 4 minutes
• 4. Improving Model Fit • 3 minutes
• 5. Model Selection • 3 minutes
• 6. Challenges of Predictive Modeling • 5 minutes
• 7. How to Build a Model using XLMiner • 8 minutes

2 quizzes • Total 70 minutes

• Week 2 Quiz • 30 minutes
• Week 2 Application Assignment • 40 minutes
• Reflection on Statistical Techniques • 10 minutes

Predicting a Binary Outcome

This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.

8 videos • Total 32 minutes

• 0. Introduction to classification • 1 minute • Preview module
• 1. Introduction to Logistic Regression • 4 minutes
• 2. Building Logistic Regression Model • 6 minutes
• 3. Multiple Logistic Regression • 3 minutes
• 4. Cross Validation and Confusion Matrix • 5 minutes
• 5. Cost Sensitive Classification • 2 minutes
• 6. Comparing Models Independent of Costs and Cutoffs • 3 minutes
• 7. Building Logistic Regression Models using XLMiner • 6 minutes
• Week 3 Quiz • 30 minutes
• Week 3 Application Assignment • 30 minutes
• The Best Prediction Method • 10 minutes

Trees and Other Predictive Models

This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.

8 videos 3 quizzes 1 peer review 1 discussion prompt

8 videos • Total 31 minutes

• 0.Introduction to Advanced Predictive Modeling Techniques • 1 minute • Preview module
• 1. Introduction to Trees • 2 minutes
• 2. Classification Trees • 5 minutes
• 3. Regression Trees • 2 minutes
• 4. Bagging, Boosting, Random Forest • 4 minutes
• 5. Building Trees with XLMiner • 5 minutes
• 6. Neural Networks • 5 minutes
• 7. Building Neural Networks using XLMiner • 4 minutes

3 quizzes • Total 90 minutes

• Final Course Assignment Quiz • 30 minutes
• Week 4 Quiz • 30 minutes
• Week 4 Application Assignment • 30 minutes

1 peer review • Total 120 minutes

• Final Course Assignment Peer Review • 120 minutes
• Reflection: Trees & Neural Networks • 10 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Recommended if you're interested in Data Analysis

University of Colorado Boulder

Advanced Business Analytics Capstone

Communicating Business Analytics Results

Business Analytics for Decision Making

University of Colorado System

Machine Learning for Marketers

Why people choose coursera for their career.

Learner reviews

Showing 3 of 590

590 reviews

Reviewed on May 18, 2019

The tutor organised the course with clear points and highlights questions during videos. The only confusion is about web version xlminer tool.

Reviewed on Apr 15, 2020

Good course to give a basic understanding of predictive modelling and analytics. Good assignments and opportunity to review peer submissions help reinforce the learnings.

Reviewed on Feb 16, 2017

Its an excellent course and thanks to Professor for making this course so practice oriented.

New to Data Analysis? Start here.

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions

When will i have access to the lectures and assignments.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions